The plotly package. A godsend for interactive documents, dashboard and presentations. For such documents there is no doubt that anyone would prefer a plot created in plotly rather than ggplot2. Why? Using plotly gives you neat and crucially interactive options at the top, where as ggplot2 objects are static. In an app we have been devloping here at Jumping Rivers, we found ourselves asking the question would it be quicker to use plot_ly or wrapping a ggplot2 object in ggplotly? I found the results staggering.

Prerequisites

Throughout we will be using the packages: dplyr, ggplot2, plotly and microbenchmark. The data in use is the birthdays dataset in the mosaicData package. Containing the counts of births in USA on each day from 1969 - 1988.

install.packages("mosaicData")
library(mosaicData)
install.packages("dplyr")
library(dplyr)
install.packages("ggplot2")
library(ggplot2)
install.packages("plotly")
library(plotly)
install.packages("microbenchmark")
library(microbenchmark)

Analysis

Let’s take a look at the data.

b = mosaicData::Birthdays
head(b)
##   state year month day       date wday births
## 1    AK 1969     1   1 1969-01-01  Wed     14
## 2    AL 1969     1   1 1969-01-01  Wed    174
## 3    AR 1969     1   1 1969-01-01  Wed     78
## 4    AZ 1969     1   1 1969-01-01  Wed     84
## 5    CA 1969     1   1 1969-01-01  Wed    824
## 6    CO 1969     1   1 1969-01-01  Wed    100

Let’s start off with a very simple scatter graph of the mean births in every year.

meanb = b %>% group_by(year) %>% summarise(mean = mean(births))
ggplotly(ggplot(meanb) + 
  geom_point(aes(y = mean, x = year, colour = year)))

plot_ly(data = meanb, 
                 y = ~mean, x = ~year, color = ~year, 
                 type = "scatter")

Both graphs identical bar styling, yes?

Now let’s use microbenchmark to see how their timings compare.

time = microbenchmark::microbenchmark(
        ggplotly = ggplotly(ggplot(meanb) + 
                            geom_point(aes(y = mean, x = year, colour = year))),
          plotly = plot_ly(data = meanb, 
                           y = ~births, x = ~year, 
                           color = ~year, type = "scatter"),
                           times = 100, unit = "s")
time
## Unit: seconds
##      expr         min          lq       mean      median          uq
##  ggplotly 0.060465833 0.064966002 0.08341875 0.067533562 0.070341152
##    plotly 0.005938955 0.006632689 0.00771174 0.007236614 0.008009191
##         max neval cld
##  1.60127363   100   b
##  0.03457796   100  a
autoplot(time)

Now I thought nesting a ggplot object within ggplotly() would be slower than using plot_ly(), but I didn’t think it would be this slow. On average ggplotly() is 11 times slower than plot_ly(). 11! One run even took 1.601 seconds!

Let’s take it up a notch. There we were just plotting 20 points, what about if we plot over 20,000? Here we will plot the min, mean and max births on each day.

meandate = b %>% group_by(date) %>% summarise(births = mean(births))
maxdate = b %>% group_by(date) %>% summarise(births = max(births))
mindate = b %>% group_by(date) %>% summarise(births = min(births))
all = rbind(meandate, maxdate, mindate)

all$stat = rep(c("mean","max", "min"), each = 7305)

ggplotly(ggplot(all) + geom_point(aes(y = births, x = date, colour = stat)))

plot_ly(all, x = ~date, y = ~births, color = ~stat, type = "scatter")

Again, both plots are identical bar styling.

time2 = microbenchmark(ggplotly = 
                                  ggplotly(ggplot(all) +
                                          geom_point(aes(y = births, x = date, colour = stat))),
                                 plotly = plot_ly(all, x = ~date, y = ~births, 
                                           color = ~stat, type = "scatter"),
                                   times = 100, unit = "s")
time2
## Unit: seconds
##      expr         min          lq        mean      median          uq
##  ggplotly 0.342929589 0.362489416 0.382971125 0.370923618 0.380537679
##    plotly 0.005959482 0.006483164 0.006973089 0.006705234 0.007153338
##         max neval cld
##  0.54437071   100   b
##  0.01080022   100  a
autoplot(time2)

On average ggplotly() is 55 times slower than plot_ly(), with the max run time being 0.544 seconds!